Statistical determination of cancer biomarkers: moving forward clinically

p21 as a Predictor and Prognostic Indicator of Clinical Outcome in Rectal Cancer Patients

The cell cycle plays a key and complex role in the development of human cancers. p21 is a potent cyclin-dependent kinase inhibitor (CDKI) involved in the promotion of cell cycle arrest and the regulation of cellular senescence. Altered p21 expression in rectal cancer cells may affect tumor cells' behavior and resistance to neoadjuvant and adjuvant therapy. Our study aimed to ascertain the relationship between the differential expression of p21 in rectal cancer and patient survival outcomes. Using tissue microarrays, 266 rectal cancer specimens were immunohistochemically stained for p21. The expression patterns were scored separately in cancer cells retrieved from the center and the periphery of the tumor; compared with clinicopathological data, tumor regression grade (TRG), disease-free, and overall survival. Negative p21 expression in tumor periphery cells was significantly associated with longer overall survival upon the univariate (p = 0.001) and multivariable analysis (p = 0.003, HR = 2.068). Negative p21 expression in tumor periphery cells was also associated with longer disease-free survival in the multivariable analysis (p = 0.040, HR = 1.769). Longer overall survival times also correlated with lower tumor grades (p= 0.011), the absence of vascular and perineural invasion (p = 0.001; p < 0.005), the absence of metastases (p < 0.005), and adjuvant treatment (p = 0.009). p21 expression is a potential predictive and prognostic biomarker for clinical outcomes in rectal cancer patients. Negative p21 expression in tumor periphery cells demonstrated significant association with longer overall survival and disease-free survival. Larger prospective studies are warranted to investigate the ability of p21 to identify rectal cancer patients who will benefit from neoadjuvant and adjuvant therapy.

Risk of Developing Insulin Resistance in Adult Subjects with Phenylketonuria: Machine Learning Model Reveals an Association with Phenylalanine Concentrations in Dried Blood Spots

Phenylketonuria (PKU) is an autosomal recessive inborn error of metabolism where high phenylalanine (Phe) concentrations cause irreversible intellectual disability that can be prevented by newborn screening and early treatment. Evidence suggests that PKU subjects not adherent to treatment could be at risk of insulin resistance (IR). We studied how Phe concentrations (PheCs) relate to IR using machine learning (ML) and derived potential biomarkers. In our cross-sectional study, we analyzed subjects with neonatal diagnoses of PKU, grouped as follows: 10 subjects who adhered to treatment (G1); 14 subjects who suspended treatment (G2); and 24 control subjects (G3). We analyzed plasma biochemical variables, as well as profiles of amino acids and acylcarnitines in dried blood spots (DBSs). Higher PheCs and plasma insulin levels were observed in the G2 group compared to the other groups. Additionally, a positive correlation between the PheCs and homeostatic measurement assessments (HOMA-IRs) was found, as well as a negative correlation between the HOMA-Sensitivity (%) and quantitative insulin sensitivity check index (QUICKI) scores. An ML model was then trained to predict abnormal HOMA-IRs using the panel of metabolites measured from DBSs. Notably, ranking the features' importance placed PheCs as the second most important feature after BMI for predicting abnormal HOMA-IRs. Our results indicate that low adherence to PKU treatment could affect insulin signaling, decrease glucose utilization, and lead to IR.